Introduction to recommender systems

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Recommender systems try to provide people with recommendations of items they will appreciate, based on their past preferences, history of purchase, and demographic information. This chapter (1) introduces recommender systems, classifying them along four dimensions (i.e. the way the preferences are gathered, the used approach, the type of algorithm, and the way the results are provided) and describing recent work done in the area, and (2) provides more details about one such type of recommender systems, namely collaborative-recommendation systems. Such systems work by analyzing the items previously rated by all the users and are not based on the content of the items, as content-based systems.